tab:Kitti2015accuracy lists the quantitative results of the accuracy of different configurations of our implementations, as well as those of other approaches and implementations, which are achieved on the KITTI 2015 stereo benchmark. While the results of our approach were achieved on the training set of the benchmark, the results of the approaches from literature were taken either from the corresponding publication or the official listing of the benchmark, which lists the results achieved on the actual test set. The upper part of the tab:Kitti2015accuracy lists algorithms and configurations, which are optimized for the deployment and execution on embedded hardware. Not all of these are variants of the *SGM stereo algorithm. Yet, they serve as a good comparison since they were deployed on the same or similar hardware as ours. The three algorithms at the bottom of the list serve as a baseline to our approaches. While the one from [1]} reveals the accuracy achieved by the original *SGM algorithm implemented on the GPU with the *CT of unknown size as a cost function, the SGBM variant of the OpenCV library is widely spread and easy to use, however, not optimized for embedded processing. The algorithm of [2]} is evaluated on both the KITTI 2015 stereo benchmark, as well as the Middlebury 2014 stereo benchmark. They propose to use a random forest classifier to learn to efficiently fuse the different scan-line optimizations of the *SGM algorithm, in order to reduce the number of optimization paths for embedded processing more efficiently.
